Alter Built Zero-Trust Security for AI Agents Because Nobody Else Did

The Macro: AI Agents Have the Keys to Everything and Nobody Checked Their ID

Here is something that should concern you. Right now, AI agents in production environments are making tool calls with long-lived API keys that have far more permissions than the agent needs. An agent that should only read from a database has write access. An agent that should only process payments under fifty dollars can process payments of any amount. An agent that should only access one customer’s data can access all customer data.

This is not a hypothetical. This is the default configuration for most AI agent deployments in 2026. The agent gets an API key. The API key has broad permissions. The agent makes calls. Nobody verifies whether the specific parameters of each call are authorized. Nobody issues short-lived credentials scoped to the exact action the agent needs to perform. Nobody audits the calls in real time with enough granularity to catch policy violations before they hit production systems.

The traditional identity and access management stack was not built for this. Okta, Auth0, and CyberArk handle human authentication well. They verify that a person is who they claim to be and grant access based on roles. But AI agents are not humans. They do not log in with a password. They do not have sessions that expire when they close their browser. They make hundreds of tool calls per minute, each of which might need different permissions, and they operate continuously.

HashiCorp Vault manages secrets and can issue dynamic credentials, but it was designed for infrastructure, not for the granular, per-call authorization that AI agents require. Open Policy Agent handles policy enforcement but requires you to write and maintain all the policies yourself. Neither tool was designed to intercept an AI agent’s tool call, inspect the parameters, and decide in real time whether that specific call with those specific parameters should be allowed.

The security gap is widening as fast as AI agent adoption is growing. Every company deploying agents with MCP connectors, API integrations, or database access is running an implicit trust model. The agent is trusted because the developer who deployed it is trusted. That is the same logic that led to the most expensive security breaches of the last decade.

The Micro: Intercepting Every Tool Call, Every Time

Srikar Dandamuraju and Kevan Dodhia founded Alter in New York out of Y Combinator’s Summer 2025 batch. Dandamuraju is the CEO. Dodhia is the CTO. The team is two people. They partner with former OpenAI cybersecurity experts for red teaming services, which tells you they take the threat model seriously.

The product intercepts every AI agent tool call and applies three layers of control. First, identity verification. The agent must prove who it is and what it is authorized to do before any call reaches the target system. Second, parameter-level authorization. Not just “can this agent access the database” but “can this agent run this specific query with these specific parameters against this specific table.” Third, real-time guardrails that prevent destructive or unauthorized actions before they execute. Drop a table? Blocked. Transfer funds exceeding a policy limit? Blocked. Access data outside the agent’s scope? Blocked.

The credential model is what separates this from bolted-on security. Alter issues ephemeral, scope-narrowed tokens that live for seconds. The agent never holds a long-lived API key. Every tool call gets a credential that is scoped to exactly that action and expires immediately after. This is how security should work in an agent-first world, and it is how almost nobody is doing it today.

They support MCP and native tool integrations for connecting enterprise systems. The policy engine handles both RBAC (role-based) and ABAC (attribute-based) access control. The dashboards are designed for CISOs and compliance teams, with real-time audit logs that map every agent action to a policy decision. The compliance story covers SOC 2, HIPAA, and GDPR.

The positioning is specific enough to be credible. They are not trying to be a general-purpose security platform. They are building the identity and authorization layer for AI agents specifically. That focus is a strength at this stage.

The Verdict

I think Alter is solving the right problem at the right time, and I think most companies deploying AI agents do not yet realize how exposed they are. The AI agent security market barely exists as a category. That is both the opportunity and the challenge. When you are creating a category, you have to educate buyers before you can sell to them.

The competitive risk is real but manageable. If Okta or Auth0 builds an AI agent module, they will have distribution advantages. But identity companies tend to move slowly and optimize for their existing customer workflows. The parameter-level, per-call authorization that Alter provides is a different architectural approach than anything in the traditional IAM stack. Bolting it on as a feature is harder than it sounds.

Thirty days, I want to see how many companies are running Alter in production and what types of agents they are securing. Sixty days, whether the ephemeral credential model creates any latency issues at scale that affect agent performance. Ninety days, the question is whether a high-profile AI agent security incident drives urgent demand for this category or whether adoption stays gradual. If a major breach traces back to an over-permissioned AI agent, Alter’s phone will ring off the hook. If the market stays quiet, they will need to drive demand through education and compliance requirements. Either way, the product is needed. The only question is how fast the market agrees.